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Digital twins (DTs) promise innovation for the marine renewable energy sector using modern technological advances and the existing maritime knowledge frameworks. The DT is a digital equivalent of a real object that reflects and predicts its behaviours and states in a virtual space over its lifetime. DTs collect data from multiple sources in pilots and leverage newly introduced low-cost sensor systems. They synchronize, homogenize, and transmit the data to a central hub and integrate it with predictive and learning models to optimize plant performance and operations. This research presents critical aspects of DT implementation challenges in marine energy digitalization DT approaches that use and combine data systems. Firstly, the DT and the existing framework for marine knowledge provided by systems are presented, and the DT's main development steps are discussed. Secondly, the DT implementing main stages, measurement systems, data harmonization and preprocessing, modelling, comprehensive data analysis, and learning and optimization tools, are identified. Finally, the ILIAD (Integrated Digital Framework for Comprehensive Maritime Data and Information Services) project has been reviewed as a best EU funding practice to understand better how marine energy digitalization DT's approaches are being used, designed, developed, and launched.

Standard BIM data-exchange format requirements are permeating Building Construction. This is due to the increasing digital communication between Stakeholders of complex projects. All steps of development (conception, design, procurement, construction and operation) are pushing towards integration of data schemas. Desirably, knowledge and value generated by one stakeholder may then be leveraged by other stakeholders. 3D models, simulations, measurements, logbooks, schedules, as-built conditions and other information constructs are on the verge of interoperable communication. Structural Analysis must play a significant role within those more digitized data exchanges. This paper presents recent developments of frameworks, connectors and tools for the integration of structural I-shaped beam entities into Digital Twins. The information construct I-Twin encompasses information loads for geometries (IFC-Based), sensors (IoT) and simulations (FEM)., Authors acknowledge the funding of ASHVIN, “Assistants for Healthy, Safe, and Productive Virtual Construction Design, Operation & Maintenance using a Digital Twin” an H2020 project under agreement 958161. This publication reflects only authors' view and that the European Commission is not responsible for any uses that may be made of the information it contains., Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement Econòmic, Postprint (published version)

In the H2020 European project ASHVIN “Assistants for Healthy, Safe, and Productive Virtual Construction Design, Operation & Maintenance using a Digital Twin”, a set of Key Performance Indicators (KPIs) and Performance Indicators (PIs) to plan and control productive, resource efficient, and safe maintenance are being developed for transport infrastructure. This paper is presenting PIs and KPIs for the assessment and monitoring of the following aspects: Productivity, Resource Efficiency, Cost, Health & Safety during the operational life cycle stage, which is mainly focusing on the inspection and maintenance planning. Quantifiable and measurable PIs and KPIs are proposed and applied on two demonstration projects, highway bridge in Spain and airport runway in Croatia, as part of transportation infrastructure. Proposed PIs and KPIs are integrated into digital twins of the analyzed assets and into decision making tools for risk based maintenance planning. This paper presents the overview of the proposed digital PIs and KPIs applied on two demonstration projects and the integration into decision support tools for efficient and sustainable maintenance planning., Postprint (published version)

This paper presents a digital-twin based tool that can be used for bridge management. The tool offers various capabilities that include embedding BIM geometries, simulations, measurements, GIS location, and performance analysis. These features enable efficient and effective management of bridges by providing detailed information and analysis of the bridge's performance, which can help in identifying potential issues and implementing appropriate solutions. The methodology represents one of the outcomes of Ashvin, an H2020 project aimed to providing assistant solution for design, construction and management of infrastructure systems. The hitherto obtained findings include this digital tool which can be of practical and beneficial use for bridge owners and operators, as it can help them to organize and centralize the bridge data in a way it helps making informed decisions regarding the maintenance, repair, and replacement of bridges. Overall, this paper highlights the importance of embracing technology in the management of critical infrastructure such as bridges, which can significantly improve their performance and extend their lifespan., European Commission. Grant Number: 958161, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles, Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement Econòmic, Postprint (published version)

Biopharmaceuticals are of ever-growing importance for human health. The development and production of biopharmaceuticals has the potential for efficiency improvements by switching from batch-wise to continuous production. This change demands more advanced process control in the form of automation and digital tools such as digital twins, which in turn need adequate mathematical models and accessible process data.This thesis discusses the concept of digital twins as well as the automation of modelling and data acquisition in integrated continuous downstream processes for the development and production of biopharmaceuticals. Utilizing in-house supervisory control software for chromatographic systems enables automation of modeling, calibration, sampling, and analysis in integrated downstream processes, advancing the development of digital twins.

Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.

There is an urgent need to make the way in which people and goods move more sustainable. Though pavements are one of the key components of road transportation networks and logistics systems, they are usually overlooked in systemic sustainability analyses. This means major downtimes for ad-hoc reparations and a general practice of higher resource and energy expenditure in critical transportation infrastructure, which in turn even leads to adverse consequences for actors in the transportation network. Though technological developments are rapidly advancing in the transport-, sensing-, and computational fields, such advances are rarely systemically implemented or exploited for infrastructure maintenance planning. Technologies that make the road itself 'smart' are still in a pilot- or project phase and are still generally underutilized on a larger scale. There is a significant risk that possible sustainability benefits on changes in the vehicle side (e.g., towards an autonomous or electrical vehicle fleet) are overshadowed by the continuous sustainability losses from the lack of changes on the infrastructure maintenance side. Pathways are therefore needed to show how such technologies can be embedded in the daily and long-term management of transport infrastructure to improve the sustainability of the entire transportation chain. For this reason, the ELISA project (Energy Effective Logistics and Infrastructure Systems Assessment for Cargo Port) started in 2019, with a focus on the recently built port of Norvik in the south of Stockholm for the development of a digital twin model. The project constituted a team of KTH researchers with different backgrounds that, together with stakeholders from the port, focused on developing and demonstrating how a digital twin of the port could be made as realistic as possible and thus contribute to the necessary paradigm shift. This thesis is one of the three research projects that were part of the ELISA project that, together, aimed, Det finns ett stort behov av att göra sättet på vilket människor och gods förflyttar sig mer hållbart. Även om vägbeläggning är en av de viktigaste komponenterna i vägtransportsnät och logistiksystem, så bortses de vanligtvis i systematiska hållbarhetsanalyser. Detta innebär betydande "downtime" för ad hoc-reparationer och en generell praxis med högre resurs- och energiförbrukning inom kritisk transportinfrastruktur, vilket i sin tur leder till ogynnsamma konsekvenser för aktörer inom transportnätverket. Även om teknologiska framsteg snabbt sker inom transport-, sensor- och datavetenskapliga områden, så implementeras sådana framsteg sällan systematiskt eller utnyttjas för planering av infrastrukturs underhåll. Teknologier som gör själva vägen "smart" befinner sig fortfarande i pilot- eller projektstadiet och används inte i större utsträckning. Det finns en betydande risk att potentiella hållbarhetsvinster genom förändringar på fordonssidan (till exempel mot en flotta av autonoma eller elektriska fordon) skuggas av de kontinuerliga hållbarhetsförlusterna på infrastrukturs underhållssidan. Därför behövs det sätt för att visa hur sådana teknologier kan integreras i daglig och långsiktig förvaltning av transportinfrastruktur för att förbättra hållbarheten i hela transportkedjan. Av denna anledning startade projektet ELISA (Energy Effective Logistics and Infrastructure Systems Assessment for Cargo Port) år 2019, med fokus på den nyligen byggda hamnen Norvik i södra Stockholm för utvecklingen av en digital tvillingmodell. Projektet bestod av ett team av KTH-forskare med olika bakgrunder som tillsammans med intressenter från hamnen arbetade med att utveckla och demonstrera hur en digital tvilling av hamnen kunde göras så realistisk som möjligt och därigenom bidra till det nödvändiga paradigmskiftet. Denna avhandling är en av de tre forskningsprojekt som ingick i ELISA-projektet och som tillsammans syftade till att erbjuda en systematisk väg framåt för godstransportsystem o, QC231030, ELISA

There is an urgent need to make the way in which people and goods move more sustainable. Though pavements are one of the key components of road transportation networks and logistics systems, they are usually overlooked in systemic sustainability analyses. This means major downtimes for ad-hoc reparations and a general practice of higher resource and energy expenditure in critical transportation infrastructure, which in turn even leads to adverse consequences for actors in the transportation network. Though technological developments are rapidly advancing in the transport-, sensing-, and computational fields, such advances are rarely systemically implemented or exploited for infrastructure maintenance planning. Technologies that make the road itself 'smart' are still in a pilot- or project phase and are still generally underutilized on a larger scale. There is a significant risk that possible sustainability benefits on changes in the vehicle side (e.g., towards an autonomous or electrical vehicle fleet) are overshadowed by the continuous sustainability losses from the lack of changes on the infrastructure maintenance side. Pathways are therefore needed to show how such technologies can be embedded in the daily and long-term management of transport infrastructure to improve the sustainability of the entire transportation chain. For this reason, the ELISA project (Energy Effective Logistics and Infrastructure Systems Assessment for Cargo Port) started in 2019, with a focus on the recently built port of Norvik in the south of Stockholm for the development of a digital twin model. The project constituted a team of KTH researchers with different backgrounds that, together with stakeholders from the port, focused on developing and demonstrating how a digital twin of the port could be made as realistic as possible and thus contribute to the necessary paradigm shift. This thesis is one of the three research projects that were part of the ELISA project that, together, aimed, Det finns ett stort behov av att göra sättet på vilket människor och gods förflyttar sig mer hållbart. Även om vägbeläggning är en av de viktigaste komponenterna i vägtransportsnät och logistiksystem, så bortses de vanligtvis i systematiska hållbarhetsanalyser. Detta innebär betydande "downtime" för ad hoc-reparationer och en generell praxis med högre resurs- och energiförbrukning inom kritisk transportinfrastruktur, vilket i sin tur leder till ogynnsamma konsekvenser för aktörer inom transportnätverket. Även om teknologiska framsteg snabbt sker inom transport-, sensor- och datavetenskapliga områden, så implementeras sådana framsteg sällan systematiskt eller utnyttjas för planering av infrastrukturs underhåll. Teknologier som gör själva vägen "smart" befinner sig fortfarande i pilot- eller projektstadiet och används inte i större utsträckning. Det finns en betydande risk att potentiella hållbarhetsvinster genom förändringar på fordonssidan (till exempel mot en flotta av autonoma eller elektriska fordon) skuggas av de kontinuerliga hållbarhetsförlusterna på infrastrukturs underhållssidan. Därför behövs det sätt för att visa hur sådana teknologier kan integreras i daglig och långsiktig förvaltning av transportinfrastruktur för att förbättra hållbarheten i hela transportkedjan. Av denna anledning startade projektet ELISA (Energy Effective Logistics and Infrastructure Systems Assessment for Cargo Port) år 2019, med fokus på den nyligen byggda hamnen Norvik i södra Stockholm för utvecklingen av en digital tvillingmodell. Projektet bestod av ett team av KTH-forskare med olika bakgrunder som tillsammans med intressenter från hamnen arbetade med att utveckla och demonstrera hur en digital tvilling av hamnen kunde göras så realistisk som möjligt och därigenom bidra till det nödvändiga paradigmskiftet. Denna avhandling är en av de tre forskningsprojekt som ingick i ELISA-projektet och som tillsammans syftade till att erbjuda en systematisk väg framåt för godstransportsystem o, QC231030, ELISA

Solid waste management is a global issue that affects everyone. The management of waste collection routes is a critical challenge in urban environments, primarily due to inefficient routing. This thesis investigates the use of real-time virtual replicas, namely Digital Twins to optimize waste collection routes. By leveraging the capabilities of digital twins, this study intends to improve the effectiveness and efficiency of waste collection operations. The ‘gap’ that the study aims to uncover is hence at the intersection of smart cities, Digital Twins, and waste collection routing. The research methodology comprises of three key components. First, an exploration of five widely used metaheuristic algorithms provides a qualitative understanding of their applicability in vehicle routing, and consecutively waste collection route optimization. Building on this foundation, a simple smart routing scenario for waste collection is presented, highlighting the limitations of a purely Internet of Things (IoT)-based approach. Next, the findings from this demonstration motivate the need for a more data-driven and intelligent solution, leading to the introduction of the Digital Twin concept. Subsequently, a twin framework is developed, which encompasses the technical anatomy and methodology required to create and utilize Digital Twins to optimize waste collection, considering factors such as real-time data integration, predictive analytics, and optimization algorithms. The outcome of this research contributes to the growing concept of smart cities and paves the way toward practical implementations in revolutionizing waste management and creating a sustainable future., Sophantering är ett globalt problem som påverkar alla, och hantering av sophämtningsrutter är en kritisk utmaning i stadsmiljöer. Den här avhandlingen undersöker användningen av virtuella kopior i realtid, nämligen digitala tvillingar, för att optimera sophämtningsrutter. Genom att utnyttja digitala tvillingars förmågor, avser den här studien att förbättra effektiviteten av sophämtning. Forskningsmetoden består av tre nyckeldelar. Först, en undersökning av fem välanvända Metaheuristika algoritmer som ger en kvalitativ förståelse av deras applicerbarhet i fordonsdirigering och således i optimeringen av sophämtningsrutter. Baserat på detta presenteras ett enkelt smart ruttscenario för sophämtning som understryker bristerna av att bara använda Internet of Things (IoT). Sedan motiverar resultaten av demonstrationen nödvändigheten för en mer datadriven och intelligent lösning, vilket leder till introduktionen av konceptet med digitala tvillingar. Därefter utvecklas ett ramverk för digitala tvillingar som omfattar den tekniska anatomin och metod som krävs för att skapa och använda digitala tvillingar för att optimera sophämtningsrutter. Dessa tar i beaktning faktorer såsom realtidsdataintegrering, prediktiv analys och optimeringsalgoritmer. Slutsatserna av studien bidrar till det växande konceptet av smarta städer och banar väg för praktisk implementation i revolutionerande sophantering och för skapandet för en hållbar framtid.

The world is facing a series of new societal and industrial challenges, such as continuously increasing costs in food and energy supply and a short supply of skilled labor. To solve these new challenges, operation, information, and communication technology is entering a new era with more focus targeted to cost reduction and energy savings. In this article, how innovative technologies including virtualization, low-code development, digital twins, and industrial agents can support and impact digital and green transitions is analyzed. In addition, how these technologies will help the security and sustainability of future industrial automation systems is also discussed.

Purpose: The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process. Design/methodology/approach: A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights. Findings: The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector. Practical implications: The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry. Originality/value: The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

Purpose: The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities. Design/methodology/approach: Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet. Findings: Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage. Practical implications: The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM. Originality/value: The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

Bygg- och fastighetsbranschen präglas av långsam digitaliserings takt där bristande dokumentation förekommer och i vissa fall försvårar beslutsfattningsmoment. Digitala tvillingar är kända för att effektivisera fastighetsförvaltning och bidra till bättre beslutsfattning. Trots det är det många som inte har hört talas om digitala tvillingar. Mot bakgrund av att det är ett outforskat område undersöker denna studie beslutsfattningsmomentet med digital tvilling inom bygg- och fastighetsbranschen. Studiens resultat baseras på intervjuer med ett stort byggföretag och en digital-tvilling-leverantör. Det framgår att en digital tvilling kan bestå av en eller flera av delarna: 3D- scanning, sensorer samt tilläggsfunktioner såsom analyser, styrning och olika applikationer, varav definitionen av en digital tvilling varierar. Resultatet visar att 3D scanning är särskilt lämpligt när det kommer till ombyggnationer och relationshandlingar (det verkliga utförandet av byggnaden), varav sensorer är användbara inom beslutsfattning utifrån prediktivt underhåll, mätning av nyttjande och energiuppföljning. Vidare framkommer det att tillägg av analyser kan föreslå lämpliga beslut och att styrning kan möjliggöra digital fastighetsskötsel, medan AI moduler kan ersätta den mänskliga beslutsfattningen. Utifrån det empiriska materialet som framkom tillämpades situationsmedvetenhet som teoretiskt ramverk för att förklara hur digitala tvillingar kan bidra med dokumentation som leder till bättre uppfattning och förståelse för omgivningen, vilket möjliggör mer välgrundade beslut. Slutligen konstaterar studien att utformningen av digitala tvillingar och dess funktioner avgör hur de kan användas vid beslutsfattning, något som gör kravställningen av dessa till en avgörande aspekt., The construction and real estate industry is characterized by a slow rate of digitization and lack of documentation, which in some cases makes decision-making difficult. Digital twins are known to contribute to more efficient property management and better decision-making. Despite this, many people have not heard of digital twins. Since it is an unexplored area, this study examines the decision-making with digital twins in the construction and real estate industry. The study's results are based on interviews with a large construction company and a digital twin supplier. It appears that a digital twin can consist of one or more of the parts: 3D scanning, sensors and additional functions such as analyses, control and various applications, where the definition of a digital twin varies. The result shows that 3D scanning is particularly suitable when it comes to reconstruction and as-built documents. Sensors appear to be useful in decision-making according to predictive maintenance, measurement of utilization and energy follow-up. Furthermore, it appears that digital twin analyses can suggest appropriate decisions and that control can enable digital property management, while AI modules can replace human decision-making. Based on the empirical material, situational awareness was applied as a theoretical framework to explain how digital twins can contribute with documentation that leads to a better perception and understanding of the environment, enabling more informed decisions. The study thus states that the design of digital twins determine how they can be used in decision-making, something that makes the requirements of these a vital aspect.

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.

Regenerated commercial heritage buildings employ heating, ventilation, and air conditioning (HVAC) systems that significantly increase their energy consumption, necessitating a methodology that supports low-energy operation to achieve a sustainable built environment. Poor indoor air quality (IAQ) management can cause irreversible damage to heritage buildings. However, there are risks of the destruction of inherent heritage values if energy-efficiency approaches are implemented without considering the visual impacts and multiple IAQ parameters in heritage buildings. To preserve heritage buildings, this study developed a multi-indicator adaptive ventilation control system for IAQ management using digital twin technology, which consisted of triggers and feedback. A digital representation of heritage buildings was established using Heritage Building Information Modelling (HBIM) with sensors to trigger adjustments in ventilation system settings. The sensor placement rules for IAQ monitoring and HVAC control of heritage buildings were demonstrated using computational fluid dynamics (CFD) simulations. The relationships among the HVAC inlet velocity, multiple IAQ parameters, and energy consumption were quantified using CFD and energy simulations. Simulation data were used to generate responsive charts for adaptive ventilation control, and the sensor data provided feedback to the ventilation system. The optimal ventilation system control strategy achieved up to 30% energy savings in the illustrative example. The proposed multi-indicator adaptive HVAC control system contributes significantly to the timely reduction of multiple air pollutants, IAQ parameter adjustments for the preservation of heritage buildings with minimal structural and visual impacts, and the need for more autonomous and energy-efficient HVAC systems. © 2023 Elsevier Ltd

[Resumen] Actualmente se está produciendo una transformaci´on digital en la industria gracias a la incorporaci´on de diversas tecnologías habilitadoras como la automatización, robótica, computación en la nube, ciberseguridad industrial, integración de sistemas o gemelos digitales, entre otras. Esta última está generando un gran interés por el valor añadido que supone incorporar simulaciones realistas y completamente operativas de los procesos. En este trabajo, se propone una metodología para desarrollar gemelos digitales, en los que se incorporan, además, otras tecnologías habilitadoras como la integración de sistemas o la computación en la nube. Adicionalmente, se presenta una aplicación desarrollada con Unity3D donde se emplea dicha metodología para obtener un gemelo digital de una célula electro-neumática robotizada., [Abstract] A digital transformation is currently taking place in industry thanks to the incorporation of various enabling technologies, such as automation, robotics, cloud computing, industrial cybersecurity, systems integration or digital twins, among others. The latter is generating great interest due to the added value of incorporating realistic and fully operational simulations of the processes. This paper proposes a methodology for developing digital twins, which also incorporates other enabling technologies such as systems integration or cloud computing. In addition, an application developed with Unity3D is presented where this methodology is used to obtain a digital twin of a robotic electro-pneumatic cell.

[Resumen] Los blooms de cianobacterias son procesos biológicos dinámicos, peligrosos para los habitantes y usuarios de los recursos hídricos, ya que reducen el oxigeno y el alcance de la luz de las masas del agua, y pueden producir toxinas altamente dañinas. La monitorización y la predicción automática de los blooms pueden permitir a las autoridades detectarlos y anticipar la toma de decisiones para mitigar los riesgos que éstos conllevan. El uso de tecnologías como el Modelado & Simulación, los Vehículos Autónomos de Superficie inteligentes, el Internet de las Cosas con computación en el borde, los Gemelos Digitales y la Inteligencia Artificial facilita tanto la monitorización como la predicción y la gestión basada en el conocimiento de la situación real de las masas de agua. Este artículo presenta diferentes avances y proyectos de investigación en estos campos del grupo de Ingeniería de Sistemas, Control, Automatización y Robótica de la Universidad Complutense de Madrid., [Abstract] Cyanobacterial blooms are dynamic biological processes, dangerous for inhabitants and users of water resources, as they reduce the oxygen and light reach in water bodies, and can produce highly harmful toxins. Automatic monitoring and prediction of the blooms can enable authorities to detect them and anticipate their decisions to mitigate the risks that they pose. The use of technologies such as Modeling & Simulation, intelligent Autonomous Surface Vehicles, Internet of Things with edge computing, Digital Twins and Artificial Intelligence facilitates monitoring, prediction and knowledge-based management of the real situation of water bodies. This article presents different advances and research projects in these fields by the Systems Engineering, Control, Automation and Robotics group of the Complutense University of Madrid.

Background: Digital twins (DT) are the coupling of a real-world physical asset to a virtual representation to provide insight and actionable knowledge. The benefits of DT are considered to include improvements in reproducibility, reliability of interventions, increased productivity, as well as increased time for innovation. For instance, a DT could be used to boost agricultural productivity whilst also meeting various targets (e.g., biodiversity, sustainability). Or a DT could be used to monitor a cell culture, predict interactions, and make subtle adjustments to maintain the environment allowing researchers to conduct other work. Yet in developing DT two fundamental questions emerge: ‘What will the DT capabilities be?’ (i.e., the range of features and possible actions) and ‘What will the DT do?’ (i.e., which capabilities will it utilise). Methods: Here we discuss a theoretical framework for DTs developed during Wageningen University & Research’s Investment Programme on DTs that aims to answer these questions. Focusing on the Life and Environmental Sciences to help developers and stakeholders to agree on the capabilities, purpose, and goal of a DT. As well as identifying iterative design stages that may help set interim development goals such as a minimum viable product.Results: This framework defines a DT as sitting at one of five maturity, or capability, levels associated with specific types of DT: a status, an informative, a predictive, an optimisation, and an autonomous twin.Conclusions: The aim of DTs is to make better, data-driven, decisions yet there can be a gulf between expectations of what a Digital Twin will do and the reality. The five maturity levels outlined here can be used to first identify and communicate about the type of Digital Twin required for a particular project prior to DT development. Bridging the gap between what project leads, developers, and stakeholders envision the end-product will be.

High-fidelity (computer-) experiments are very expensive to perform, hence extracting as much information as possible from the collected data is vital. We present methods to estimate experiment outcomes based on ingested past data, to steer further sample acquisition both by suggesting paths for maximized uncertainty reduction and highlighting sensitivities on inputs, with use-cases from laser-light propagation and laser-plasma interactions to electron-bunch kinetics.

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one's field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology.

There is an urgent need to make the way in which people and goods move more sustainable. Though pavements are one of the key components of road transportation networks and logistics systems, they are usually overlooked in systemic sustainability analyses. This means major downtimes for ad-hoc reparations and a general practice of higher resource and energy expenditure in critical transportation infrastructure, which in turn even leads to adverse consequences for actors in the transportation network. Though technological developments are rapidly advancing in the transport-, sensing-, and computational fields, such advances are rarely systemically implemented or exploited for infrastructure maintenance planning. Technologies that make the road itself 'smart' are still in a pilot- or project phase and are still generally underutilized on a larger scale. There is a significant risk that possible sustainability benefits on changes in the vehicle side (e.g., towards an autonomous or electrical vehicle fleet) are overshadowed by the continuous sustainability losses from the lack of changes on the infrastructure maintenance side. Pathways are therefore needed to show how such technologies can be embedded in the daily and long-term management of transport infrastructure to improve the sustainability of the entire transportation chain. For this reason, the ELISA project (Energy Effective Logistics and Infrastructure Systems Assessment for Cargo Port) started in 2019, with a focus on the recently built port of Norvik in the south of Stockholm for the development of a digital twin model. The project constituted a team of KTH researchers with different backgrounds that, together with stakeholders from the port, focused on developing and demonstrating how a digital twin of the port could be made as realistic as possible and thus contribute to the necessary paradigm shift. This thesis is one of the three research projects that were part of the ELISA project that, together, aimed, Det finns ett stort behov av att göra sättet på vilket människor och gods förflyttar sig mer hållbart. Även om vägbeläggning är en av de viktigaste komponenterna i vägtransportsnät och logistiksystem, så bortses de vanligtvis i systematiska hållbarhetsanalyser. Detta innebär betydande "downtime" för ad hoc-reparationer och en generell praxis med högre resurs- och energiförbrukning inom kritisk transportinfrastruktur, vilket i sin tur leder till ogynnsamma konsekvenser för aktörer inom transportnätverket. Även om teknologiska framsteg snabbt sker inom transport-, sensor- och datavetenskapliga områden, så implementeras sådana framsteg sällan systematiskt eller utnyttjas för planering av infrastrukturs underhåll. Teknologier som gör själva vägen "smart" befinner sig fortfarande i pilot- eller projektstadiet och används inte i större utsträckning. Det finns en betydande risk att potentiella hållbarhetsvinster genom förändringar på fordonssidan (till exempel mot en flotta av autonoma eller elektriska fordon) skuggas av de kontinuerliga hållbarhetsförlusterna på infrastrukturs underhållssidan. Därför behövs det sätt för att visa hur sådana teknologier kan integreras i daglig och långsiktig förvaltning av transportinfrastruktur för att förbättra hållbarheten i hela transportkedjan. Av denna anledning startade projektet ELISA (Energy Effective Logistics and Infrastructure Systems Assessment for Cargo Port) år 2019, med fokus på den nyligen byggda hamnen Norvik i södra Stockholm för utvecklingen av en digital tvillingmodell. Projektet bestod av ett team av KTH-forskare med olika bakgrunder som tillsammans med intressenter från hamnen arbetade med att utveckla och demonstrera hur en digital tvilling av hamnen kunde göras så realistisk som möjligt och därigenom bidra till det nödvändiga paradigmskiftet. Denna avhandling är en av de tre forskningsprojekt som ingick i ELISA-projektet och som tillsammans syftade till att erbjuda en systematisk väg framåt för godstransportsystem o, QC231030, ELISA

In recent years, there has been a growing interest in adopting circular approaches in the built environment, specifically reusing existing buildings or their components in new projects. To achieve this, drawings, laser scanning, photogrammetry and other techniques are used to capture data on buildings and their materials. Although previous studies have explored scan-to-BIM workflows, automation of 2D drawings to 3D models, and machine learning for identifying building components and materials, a significant gap remains in refining this data into the right level of information required for digital twins, to share information and for digital collaboration in designing for reuse. To address this gap, this paper proposes digital guidelines for reusing precast concrete based on the level of information need (LOIN) standard EN 17412-1:2020 and examines several CAD and BIM modelling strategies. These guidelines can be used to prepare digital templates that become digital twins of existing elements, develop information requirements for use cases, and facilitate data integration and sharing for a circular built environment., Part of ISBN 9789491207358QC 20231013, ReCreate project

Excessive and fluctuating indoor air quality (IAQ) leads to destruction of historical buildings. Regenerated commercial historic buildings are generally fitted with heating, ventilation, and air conditioning (HVAC) systems for IAQ improvement. There is a time delay in the HVAC system to control environment within the target range after the settings adjustment because of the time required for air conditioners to cool, heat or exchange fresh air. The lack of timely and effective control of traditional HVAC is insufficient to control IAQ for the preservation of historical buildings. This research proposed a novel Multiple Output Gate Recurrent Unit (GRU) - Computational Fluid Dynamics (CFD) integration method and combined it with the automatic control using digital twin technology to improve the effectiveness and efficiency of multiple IAQ management for the preservation of whole indoor spaces. This method considered visual impacts and minimized the external equipment impact. The sequencing order of multiple IAQ enhancements by adjusting HVAC control was investigated based on the priority of multiple IAQ parameters on historic building preservation. The proposed Multiple Output GRU prediction reduced the time by at least 1/3 for training and validation. As heritage buildings restrict the installation of sensors, CFD was used to identify the maximum value and location based on sensed IAQ data for adjustment triggers. The combined method contributed to identifying the predicted maximum value to trigger the 10 min pre-regulation of the HVAC system for dealing with the time delay to maintain multiple IAQ in standard ranges for heritage building preservation. © 2023 Elsevier Ltd

Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.

This work aims to investigate the role of the resilience of Digital Twins on the applicability of the transportation system. A literature study is conducted to review the current status of research on transportation systems and Digital Twins. It is found that the current research on Digital Twins technology has achieved different degrees of success in different aspects of transportation sys-tems. Yet, the system performance of Digital Twins has to be optimized. First, the application of Digital Twins in intelligent transportation systems is analyzed. Then, how the changes in traveler behavior patterns reflect the extent to which the traffic network is affected by uncertain events is analyzed from the traveler's perspective. Finally, an Internet of Vehicles (IoV) system based on Digital Twins and blockchain is established to solve the data redundancy and high computational volume problems of in-vehicle data sharing common in the IoV system. Moreover, the perfor-mance of the twin system is optimized by proposing a multi-intelligence body algorithm based on local perception, and a case validation is performed. The results demonstrate that the adaptability of the transportation system to uncertain events and its response and recovery measures taken are reflected to some extent in the traveler behavior model. Besides, data sharing between vehicles and infrastructure in the transportation network can be well solved by Digital Twins Blockchain. The locally-aware multi-intelligent body algorithm saves more than 50% communication over-head and improves operational efficiency by nearly 20% over traditional algorithms by increasing intelligent body infrastructure units. It is adequately suited for large-scale vehicle traffic twins. It can be seen that improving the resilience of Digital Twins is a very obvious change in the adaptability of the traffic system.

Digital twins (DT) describe the integration of the physical and digital worlds with the aim of optimizing real world operations and functions. The digital twin concept has gained increasing attention across industries in the past decade including the building sector. However digital twins remain ambiguous with various existing definitions and characteristics. While DTs include all life cycle phases, ultimately their goal is optimization of operations during the use phase. Of the building life cycle phases, building facilities management (FM) is responsible for considerable costs and energy consumption and has potential for improvement through DT implementation. Along with increased building information modeling (BIM) implementation, recent advances in data driven technologies have encouraged the exploration of DT in the building sector. BIM has been coupled with technologies such as internet of things (IoT), data analytics, and cloud computing to optimize various FM functions often resembling DT. This study has reviewed existing literature on digital twins in facilities management using a structured literature review and characterized similar characteristics and definitions by different authors. Additionally, DT implementation in different FM application areas was quantified and analyzed. Results show that DT implementation in FM is still at nascent stages with major challenges surrounding standardization and data integration.

Heightened awareness of the impact of climate change has led to rapidly increasing penetration of renewable energy resources in electric energy distribution systems. Those distributed energy resources (DERs), mostly inverter-based, can act as resiliency sources for the grid but also introduce new control and stability challenges. In this thesis, a cyber-physical system (CPS) testbed is proposed combining a real-time electro-magnetic transient power system simulation and a practical model for communication network simulation. By regularly updating the CPS testbed with real-world SCADA information, a digital twin is effectively created. The digital twin allows the testing of novel microgrid control and cybersecurity strategies. Simulations using the Virginia Tech Electric Service (VTES) as a test case demonstrate the capability of adequately controlled resources, including solar PV, energy storage, and a synchronous generator, to enhance resilience by providing energy to critical loads. The DERs comply with IEEE disturbance ride-through requirements and switching transients are maintained within acceptable limits. A comprehensive DER-based resiliency plan is developed and validated for the Virginia Tech smart grid.

A digital twin (DT) can enhance construction management with comprehensive real-time simulations. However, research rarely considers prefabrication factories, whose processes have a significant impact on cost and duration. It remains unclear how construction DTs can achieve their expected benefits without dynamically interacting with the DTs of manufacturing facilities. To address this, a DT integration model is proposed. It builds upon systems theory and describes integration across the three layers objectives, processes, and data & tools. A theoretical example demonstrates potential benefits of integrated DTs. This work can assist researchers and practitioners who are focusing on DTs in the execution phase., Design & Construction Management, Integral Design & Management

To decrease the mismatch between a model and a physical system, physically interpretable model parameter values of nonlinear systems can be updated in real-time by using the Inverse Mapping Parameter Updating (IMPU) method. In this method, an Inverse Mapping Model (IMM), constituted by an Artificial Neural Network (ANN), is trained, offline, using simulated data that consists of features of output responses (ANN inputs) and corresponding parameter values (ANN outputs). In an online phase, the trained ANN can then be used to infer parameter values with high computational efficiency. The (non-trivial) choice of ANN-hyperparameters, e.g., ANN structure and training settings, may significantly influence the accuracy of the trained ANN. Therefore, this work discusses multiple ANN-hyperparameter tuning techniques to increase the accuracy of the IMPU method, of which the Bayesian search technique is the most promising considering accuracy and efficiency as it learns from previously evaluated hyperparameter values.

In recent years, there has been a growing interest in adopting circular approaches in the built environment, specifically reusing existing buildings or their components in new projects. To achieve this, drawings, laser scanning, photogrammetry and other techniques are used to capture data on buildings and their materials. Although previous studies have explored scan-to-BIM workflows, automation of 2D drawings to 3D models, and machine learning for identifying building components and materials, a significant gap remains in refining this data into the right level of information required for digital twins, to share information and for digital collaboration in designing for reuse. To address this gap, this paper proposes digital guidelines for reusing precast concrete based on the level of information need (LOIN) standard EN 17412-1:2020 and examines several CAD and BIM modelling strategies. These guidelines can be used to prepare digital templates that become digital twins of existing elements, develop information requirements for use cases, and facilitate data integration and sharing for a circular built environment., QC 20230919, ReCreate project

Datacenters are a critical part of the Internet infrastructure as they guarantee efficient deployment of a wide range of services. Since a considerable amount of datacenter failures is caused by software bugs and configuration errors, the management and testing of these networks is a crucial task. In this field, emulation-based digital twins have proven their effectiveness. To faithfully emulate the typical three layers hierarchy, composed of physical servers, virtual machines, and containers, the support for nested virtualization is a fundamental requirement. Further, the emulation of hyper-scale datacenters needs to leverage on horizontal scaling over a cluster of nodes. Existing container-based proposals do not meet both requirements. On the contrary, existing VM-based proposals meet such requirements, but they need complex configurations and high resource demands. We propose a container-based framework to faithfully emulate datacenters. This is a fundamental building block for designing datacenter digital twins, that would allow testing of real software implementations in a lightweight, scalable, and easily configurable environment., Part of ISBN 978-1-6654-7716-1QC 20230821

Tracking of building components can be instrumental in reuse for a Circular Economy. Tracking technologies (TT) for building components can be used to identify and access information for decision-making from deconstruction to design for reuse. Prior research has mainly been concerned with single technologies, limited life cycle applicability and new construction. This study aims to explore the potential of combining multiple technologies, such as QR codes, NFC, and Bluetooth tags, with BIM to support reuse along the life cycles of prefabricated concrete components. The benefits and limitations of choices in TT are examined concerning information integration in circular construction., Part of proceeding ISBN 978-0-701702-73-1QC 20230807, ReCreate project

Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce the energy consumption of BSs, inactive components of BSs, with a certain activation/deactivation time, can sleep. In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential MDP and propose an online traffic-aware deep Q-learning approach to maximize the long-term energy saving. However, there is a risk that BS is not sleeping at the right time and incurs delays to the users. To tackle this issue, we propose to use a digital twin model to encapsulate the dynamics underlying the investigated system and estimate the risk of decision-making (RDM) in advance. We define a novel metric to quantify RDM and predict the performance degradation. Mobile operators can compare the RDM with a tolerable threshold to decide on deactivating SMs, re-training, or activating SMs. We trained an agent using real traffic, obtained from an operator's BS in Stockholm. The data-set contains data rate information with very coarse-grained time granularity. Thus, we propose a scheme to generate a new data-set using the real network data-set which 1) has finer-grained time granularity and 2) considers the bursty behavior of traffic data. Simulation results show that using proposed methods, considerable energy saving is obtained at cost of negligible number of delayed users. Moreover, the proposed digital twin model can predict the performance of the DQN proactively in terms of RDM hence preventing the performance degradation in the network in anomalous situations., QC 20230509

In recent years, there has been a growing interest in adopting circular approaches in the built environment, specifically reusing existing buildings or their components in new projects. To achieve this, drawings, laser scanning, photogrammetry and other techniques are used to capture data on buildings and their materials. Although previous studies have explored scan-to-BIM workflows, automation of 2D drawings to 3D models, and machine learning for identifying building components and materials, a significant gap remains in refining this data into the right level of information required for digital twins, to share information and for digital collaboration in designing for reuse. To address this gap, this paper proposes digital guidelines for reusing precast concrete based on the level of information need (LOIN) standard EN 17412-1:2020 and examines several CAD and BIM modelling strategies. These guidelines can be used to prepare digital templates that become digital twins of existing elements, develop information requirements for use cases, and facilitate data integration and sharing for a circular built environment., QC 20230919, ReCreate project

Digital Twin (DT) in industrial cyber-physical systems (ICPS) is an essential concept for Industry 4.0. In recent years, DT architectures and frameworks for the industry have been the focus of several research initiatives regarding analytics, predictive maintenance, virtual evaluation & verification, and fault detection & diagnosis. However, the resource constraint and battery-driven devices in an ICPS environment are facing challenges such as a short life span due to high energy consumption, lower availability of the services, and low-security capabilities. This paper presents a concept of Digital Twin (DT) to be used as a proxy service for constraint devices in ICPS. We present a four-layer architectural model for the DT as a Proxy (DTaaP) that provides valuable properties for resource constraint devices such as energy efficiency, high availability & state persistence, remote & contention controlling, and security. We present a generic proof of concept (PoC) DTaaP implementation by using Eclipse Ditto which is an open-source Digital Twin Framework. We evaluate our artifact against the listed properties through an experimental scenario. We show that DTaaP improves energy efficiency, that the DT can serve as an anchor point for security, and that it provides availability and persistence for duty-cycling devices. We also identify some limitations of Eclipse Ditto for the PoC DTaaP implementation which can be a focus for future research., Funder: Arrowhead Tools (826452)ISBN för värdpublikation: 978-1-4503-9846-6

Patients not responding to therapy as expected is one of the most pressing healthcare concerns of today. It causes economical, medical and societal issues along with suffering for patients. This project aimed to address this problem and evaluate how to find the best suited drug treatments for individual patients to treat Covid-19. This project was carried out in collaboration with the company AB Mavatar, that have two networks, one experimental and one predicted, which produce drug treatment rankings differently. Different methods are used to connect drug targets to disease associated genes and thus evaluate what drugs are best suited for specific patients to treat Covid-19. The aim of this project is to examine how network, method and drug category affect the ranking of a drug treatment for four mapped Covid-19 patients. Which drug category a drug belongs to did not seem to significantly affect the drug ranking. Yet, certain drug subcategories were closely correlated. However, these subcategories were not those that are typically associated with Covid-19. The method used to connect drug targets to disease associated genes heavily impacts the ranking of the drug treatment. The methods should be further evaluated to see if some should be excluded or weighted less in drug ranking calculations. The two networks are similar in how they rank different drugs, especially in severely ill patients. Through this project and the evaluation of the impact of method choice, one can start to figure out what should be prioritized among disease related changes. Also, important parameters for personalized treatment can be evaluated., Patienter som inte svarar på terapi som förväntat är en av de största utmaningarna inom hälso- och sjukvård idag. Det orsakar ekonomiska, medicinska och samhälleliga problem samt lidande för patienter. Det här projektet adresserade detta problem och evaluerade hur man kan hitta det bäst lämpade läkemedlet för specifika patienter för att behandla Covid-19. Projektet gjordes tillsammans med företaget AB Mavatar, som har två interaktom, en experimentell och en datadriven, som rangordnar läkemedelsrekommendationer på olika sätt. Olika metoder används för att koppla samman läkemedelsmål med sjukdomsrelaterade gener och således evaluera vilka läkemedel som är bäst lämpade för specifika patienter för behandling av Covid-19. Syftet med projektet var att undersöka hur nätverk, metod och läkemedelskategori påverkar hur läkemedel rangordnas för fyra kartlagda Covid-19-patienter. Vilken läkemedelskategori ett läkemedel tillhör tycks inte märkbart påverka läkemedelsrangordning. Trots detta var vissa läkemedelsunderkategorier nära korrelerade. Dock var dessa underkategorier inte typiskt associerade med Covid-19. Metoden för att koppla samman läkemedelsmål med sjukdomsassocierade gener påverkade läkemedelsrangordningen väsentligt. Metoderna borde dock evalueras ytterligare för att eventuellt exkludera eller vikta vissa mindre i uträkningar av läkemedelsrang. De två nätverken är lika i hur de rangordnar olika läkemedel, särskilt för svårt sjuka patienter. Genom detta projekt och genom evaluering av metodvalets påverkan kan man börja begripa hur man borde priorita bland sjukdomsrelaterade förändringar. Dessutom kunde viktiga parametrar inom personlig behandling evalueras.

KPMG has recognized that large manufacturing corporates in Sweden are experiencing impaired growth possibilities and have therefore started to turn to startups to help with innovation. Corporates have traditionally focused on growth through product improvement or market expansion but have recently started to shift their focus to acquisitions and partnerships with high potential startups. However, since there are many differences between corporates and startups the collaboration can be difficult to handle, especially when it comes to integration. This study aims to investigate what types of startups large corporates are currently looking to collaborate with and to create a practical framework that can act as a guide to increase the chance of successfully integrating a startup into a corporate's operations. This study is a qualitative study that examines several Swedish manufacturing companies by delving into their specific cases. A thematic analysis, document and literature review is presented, and the results indicate that Swedish manufacturing corporates want startups with technologies that boost digital transformation (AI, IoT, digital twins) to increase predictability, traceability, and optimization. They also want to increase sustainability initiatives in terms of both technologies and materials. This is followed by a framework for how a successful corporate-startupintegration should be done. The results from the study contribute to the work of improving collaboration between startups and corporates and the results from this study can help create economic benefits, promote social sustainability, and address environmental challenges more effectively., KPMG har uppmärksammat att stora tillverkningsföretag i Sverige upplever försämrade tillväxtmöjligheter och har därför börjat vända sig till startups för att få hjälp med innovation. Företag har traditionellt fokuserat på tillväxt genom produktförbättringar eller marknadsexpansion, men har nyligen börjat skifta fokus till förvärv och partnerskap med startups med hög potential. Då det finns stora skillnader mellan stora företag och startups kan samarbetet vara svårt att hantera, särskilt när det gäller integration. Denna studie syftar till att undersöka vilka typer av startups som stora företag vill samarbeta med och att skapa ett praktiskt ramverk som kan fungera som en guide för att öka chansen att framgångsrikt integrera ett startup i företagets verksamhet. Denna studie är en kvalitativ studie som undersöker flera svenska tillverkningsföretag. En tematisk analys, dokument- och litteraturgranskning presenteras, och resultaten indikerar att svenska tillverkningsföretag vill ha startups med teknik som främjar digital transformation (AI, IoT, digitala tvillingar) för att öka förutsägbarhet, spårbarhet och optimering. De vill också öka hållbarhetsinitiativen när det gäller både teknik och material. Detta följs av ett ramverk för hur en framgångsrik integration mellan företag och startups bör gå till. Resultaten från studien bidrar till arbetet med att förbättra samarbetet mellan startups och företag och resultaten från denna studie kan bidra till att skapa ekonomiska fördelar, främja social hållbarhet och hantera miljöutmaningar mer effektivt

Understanding the needs and constraints of systems in general and a system-of-systems in particular can be challenging, yet crucial. Relying only on upfront activities will not be sufficient. Important information can be gathered around the performance and behavior of the system as well as stakeholder needs in operation. A digital twin is a way to model and understand the operation of a system. To understand the challenges and enablers related to digital twins in a system-of-systems context, we performed a literature study. In total, only 10 papers were identified that explicitly address this topic, all from the last five years, indicating that this is an active field of research. The papers revealed that definitions and terminology are unclear and that similar challenges as for systems-of-systems also exist for systems-of-digital twins. The complexity and dynamic nature of systems-of-systems motivate further study of digital twins to understand needs and constraints. However, key challenges such as concepts and principles of digital twins for systems-of-systems, cost and benefits, and evolution needs to be better understood.

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

The construction industry has a significant impact in terms of financialand environmental resources but is vastly behind other sectors in terms of digitalization.The potential of this industry to be improved by new technology has beenreflected in huge trends in research for terms such as “digital twins”. However, thepurpose of such technologies and how they can be applied to specific needs andassets in the construction sector is not always clear. This paper proposes an analysisof the purpose, current and future states of digital twins in the constructionindustry, based on a reviewof the evolution of research in the topic and recentmarketapplications. Even though there is a discrepancy between research and levelof development of tangible applications, it is undeniable that the digital transformationwill reach the construction industry. The efforts should then be focusedon technology that can be translated to its assets, such as smart management, andwill generate tangible results that can survive outside the theoretical realm.

Tracking of building components can be instrumental in reuse for a Circular Economy. Tracking technologies (TT) for building components can be used to identify and access information for decision-making from deconstruction to design for reuse. Prior research has mainly been concerned with single technologies, limited life cycle applicability and new construction. This study aims to explore the potential of combining multiple technologies, such as QR codes, NFC, and Bluetooth tags, with BIM to support reuse along the life cycles of prefabricated concrete components. The benefits and limitations of choices in TT are examined concerning information integration in circular construction., Part of proceeding ISBN 978-0-701702-73-1QC 20230807, ReCreate project

This work aims to investigate the role of the resilience of Digital Twins on the applicability of the transportation system. A literature study is conducted to review the current status of research on transportation systems and Digital Twins. It is found that the current research on Digital Twins technology has achieved different degrees of success in different aspects of transportation sys-tems. Yet, the system performance of Digital Twins has to be optimized. First, the application of Digital Twins in intelligent transportation systems is analyzed. Then, how the changes in traveler behavior patterns reflect the extent to which the traffic network is affected by uncertain events is analyzed from the traveler's perspective. Finally, an Internet of Vehicles (IoV) system based on Digital Twins and blockchain is established to solve the data redundancy and high computational volume problems of in-vehicle data sharing common in the IoV system. Moreover, the perfor-mance of the twin system is optimized by proposing a multi-intelligence body algorithm based on local perception, and a case validation is performed. The results demonstrate that the adaptability of the transportation system to uncertain events and its response and recovery measures taken are reflected to some extent in the traveler behavior model. Besides, data sharing between vehicles and infrastructure in the transportation network can be well solved by Digital Twins Blockchain. The locally-aware multi-intelligent body algorithm saves more than 50% communication over-head and improves operational efficiency by nearly 20% over traditional algorithms by increasing intelligent body infrastructure units. It is adequately suited for large-scale vehicle traffic twins. It can be seen that improving the resilience of Digital Twins is a very obvious change in the adaptability of the traffic system.

The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.

The purpose of this work is to explore the role of the safe and optimal scheduling of thermal energy storage systems in intelligent buildings in promoting sustainable economic development under Digital Twins (DTs) technology. Phase Change Material (PCM) has high energy density, constant temperature storage, small footprint, and long service life. Here, PCM is first placed in the indoor building structure, and the DTs technology is introduced. In the development of intelligent buildings, the data generated by the energy storage system of intelligent buildings in the real space can be mapped to the virtual space in real time for simultaneous analysis. In addition, the PCM wall structure and thermal network DTs model are designed for the intelligent building. In addition, the PCW structure is used to build a thermal energy storage and dispatch model of the smart thermoelectric building based on DTs. Finally, the model is evaluated and analyzed experimentally. The analysis of system optimization power under different schemes indicates that the scheduling operation strategy of thermal energy storage of building walls can avoid overcharging or over-discharging batteries in the microgrid and reduce battery power consumption. Besides, the building wall energy storage capacity is always in the range of 0.2 ∼ 0.8 on the all-weather scale. Moreover, the model constructed here achieves significantly lower economic costs, environmental costs, and energy costs and a better energy-saving effect than the existing model. The model built here can serve as experimental reference for further digital energy storage in intelligent buildings and comprehensive energy utilization because of its superior safety performance and lower consumption.

There is an increasing need for an effective monitoring strategy for the performance and condition of Down The Hole drilling. To achieve that, it is important to gain knowledge in the physics involved in Down The Hole drilling processes.A logical way to gain such understanding is to develop simulation models that are capable to represent the process. That requires physical understanding and thus, it is an iterative process. To capture the physics, it is essential to couple the axial impact, and the subsequent axial motion, with the rotation of the drill. The indentation into the rock due to impact and the subsequent resistance against rotation will couple the rotation to the axial motion and vice versa. The scenario is complicated due to the non-linear nature in the contacts and the varying stiffness of the rock.This paper presents a methodology that can be used to couple the axial and rotational motions of the DTH drills and explores the pros and cons of such a 3D simulation model.Keywords: DTH, 3D simulation, Condition monitoring

Bygg- och fastighetsbranschen präglas av långsam digitaliserings takt där bristande dokumentation förekommer och i vissa fall försvårar beslutsfattningsmoment. Digitala tvillingar är kända för att effektivisera fastighetsförvaltning och bidra till bättre beslutsfattning. Trots det är det många som inte har hört talas om digitala tvillingar. Mot bakgrund av att det är ett outforskat område undersöker denna studie beslutsfattningsmomentet med digital tvilling inom bygg- och fastighetsbranschen. Studiens resultat baseras på intervjuer med ett stort byggföretag och en digital-tvilling-leverantör. Det framgår att en digital tvilling kan bestå av en eller flera av delarna: 3D- scanning, sensorer samt tilläggsfunktioner såsom analyser, styrning och olika applikationer, varav definitionen av en digital tvilling varierar. Resultatet visar att 3D scanning är särskilt lämpligt när det kommer till ombyggnationer och relationshandlingar (det verkliga utförandet av byggnaden), varav sensorer är användbara inom beslutsfattning utifrån prediktivt underhåll, mätning av nyttjande och energiuppföljning. Vidare framkommer det att tillägg av analyser kan föreslå lämpliga beslut och att styrning kan möjliggöra digital fastighetsskötsel, medan AI moduler kan ersätta den mänskliga beslutsfattningen. Utifrån det empiriska materialet som framkom tillämpades situationsmedvetenhet som teoretiskt ramverk för att förklara hur digitala tvillingar kan bidra med dokumentation som leder till bättre uppfattning och förståelse för omgivningen, vilket möjliggör mer välgrundade beslut. Slutligen konstaterar studien att utformningen av digitala tvillingar och dess funktioner avgör hur de kan användas vid beslutsfattning, något som gör kravställningen av dessa till en avgörande aspekt., The construction and real estate industry is characterized by a slow rate of digitization and lack of documentation, which in some cases makes decision-making difficult. Digital twins are known to contribute to more efficient property management and better decision-making. Despite this, many people have not heard of digital twins. Since it is an unexplored area, this study examines the decision-making with digital twins in the construction and real estate industry. The study's results are based on interviews with a large construction company and a digital twin supplier. It appears that a digital twin can consist of one or more of the parts: 3D scanning, sensors and additional functions such as analyses, control and various applications, where the definition of a digital twin varies. The result shows that 3D scanning is particularly suitable when it comes to reconstruction and as-built documents. Sensors appear to be useful in decision-making according to predictive maintenance, measurement of utilization and energy follow-up. Furthermore, it appears that digital twin analyses can suggest appropriate decisions and that control can enable digital property management, while AI modules can replace human decision-making. Based on the empirical material, situational awareness was applied as a theoretical framework to explain how digital twins can contribute with documentation that leads to a better perception and understanding of the environment, enabling more informed decisions. The study thus states that the design of digital twins determine how they can be used in decision-making, something that makes the requirements of these a vital aspect.

Patients not responding to therapy as expected is one of the most pressing healthcare concerns of today. It causes economical, medical and societal issues along with suffering for patients. This project aimed to address this problem and evaluate how to find the best suited drug treatments for individual patients to treat Covid-19. This project was carried out in collaboration with the company AB Mavatar, that have two networks, one experimental and one predicted, which produce drug treatment rankings differently. Different methods are used to connect drug targets to disease associated genes and thus evaluate what drugs are best suited for specific patients to treat Covid-19. The aim of this project is to examine how network, method and drug category affect the ranking of a drug treatment for four mapped Covid-19 patients. Which drug category a drug belongs to did not seem to significantly affect the drug ranking. Yet, certain drug subcategories were closely correlated. However, these subcategories were not those that are typically associated with Covid-19. The method used to connect drug targets to disease associated genes heavily impacts the ranking of the drug treatment. The methods should be further evaluated to see if some should be excluded or weighted less in drug ranking calculations. The two networks are similar in how they rank different drugs, especially in severely ill patients. Through this project and the evaluation of the impact of method choice, one can start to figure out what should be prioritized among disease related changes. Also, important parameters for personalized treatment can be evaluated., Patienter som inte svarar på terapi som förväntat är en av de största utmaningarna inom hälso- och sjukvård idag. Det orsakar ekonomiska, medicinska och samhälleliga problem samt lidande för patienter. Det här projektet adresserade detta problem och evaluerade hur man kan hitta det bäst lämpade läkemedlet för specifika patienter för att behandla Covid-19. Projektet gjordes tillsammans med företaget AB Mavatar, som har två interaktom, en experimentell och en datadriven, som rangordnar läkemedelsrekommendationer på olika sätt. Olika metoder används för att koppla samman läkemedelsmål med sjukdomsrelaterade gener och således evaluera vilka läkemedel som är bäst lämpade för specifika patienter för behandling av Covid-19. Syftet med projektet var att undersöka hur nätverk, metod och läkemedelskategori påverkar hur läkemedel rangordnas för fyra kartlagda Covid-19-patienter. Vilken läkemedelskategori ett läkemedel tillhör tycks inte märkbart påverka läkemedelsrangordning. Trots detta var vissa läkemedelsunderkategorier nära korrelerade. Dock var dessa underkategorier inte typiskt associerade med Covid-19. Metoden för att koppla samman läkemedelsmål med sjukdomsassocierade gener påverkade läkemedelsrangordningen väsentligt. Metoderna borde dock evalueras ytterligare för att eventuellt exkludera eller vikta vissa mindre i uträkningar av läkemedelsrang. De två nätverken är lika i hur de rangordnar olika läkemedel, särskilt för svårt sjuka patienter. Genom detta projekt och genom evaluering av metodvalets påverkan kan man börja begripa hur man borde priorita bland sjukdomsrelaterade förändringar. Dessutom kunde viktiga parametrar inom personlig behandling evalueras.